
"""simple docstring"""





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a								=						logging.get_logger(__name__)

_a								=						{
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'bridgetower_vision_model'



		def __init__(   self							,						__a=7_68							,						__a=12							,						__a=3							,						__a=16							,						__a=2_88							,						__a=1							,						__a=1e-05							,						__a=False							,						__a=True							,						__a=False							,						**__a							,						)			->			Union[str, Any]:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    patch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    initializer_factor
							_UpperCamelCase									=    layer_norm_eps
							_UpperCamelCase									=    stop_gradient
							_UpperCamelCase									=    share_layernorm
							_UpperCamelCase									=    remove_last_layer




		@classmethod
		def 		UpperCAmelCase				(   cls							,						__a							,						**__a)			->			"PretrainedConfig":






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    cls.get_config_dict(__a							,						**__a)

							if config_dict.get('''model_type''') == "bridgetower":
												_UpperCamelCase									=    config_dict['''text_config''']

							if "model_type" in config_dict and hasattr(cls							,						'''model_type''') and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
												    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''')

							return cls.from_dict(__a							,						**__a)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'bridgetower_text_model'



		def __init__(   self							,						__a=5_02_65							,						__a=7_68							,						__a=12							,						__a=12							,						__a=1							,						__a=30_72							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=5_14							,						__a=1							,						__a=1e-05							,						__a=1							,						__a=0							,						__a=2							,						__a="absolute"							,						__a=True							,						**__a							,						)			->			Any:






							'''simple docstring'''
							super().__init__(**__a)

							_UpperCamelCase									=    vocab_size
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    initializer_factor
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    max_position_embeddings
							_UpperCamelCase									=    type_vocab_size
							_UpperCamelCase									=    layer_norm_eps
							_UpperCamelCase									=    position_embedding_type
							_UpperCamelCase									=    use_cache
							_UpperCamelCase									=    pad_token_id
							_UpperCamelCase									=    bos_token_id
							_UpperCamelCase									=    eos_token_id




		@classmethod
		def 		UpperCAmelCase				(   cls							,						__a							,						**__a)			->			"PretrainedConfig":






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    cls.get_config_dict(__a							,						**__a)

							if config_dict.get('''model_type''') == "bridgetower":
												_UpperCamelCase									=    config_dict['''text_config''']

							if "model_type" in config_dict and hasattr(cls							,						'''model_type''') and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
												    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''')

							return cls.from_dict(__a							,						**__a)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'bridgetower'



		def __init__(   self							,						__a=True							,						__a="gelu"							,						__a=7_68							,						__a=1							,						__a=1e-05							,						__a=False							,						__a="add"							,						__a=12							,						__a=6							,						__a=False							,						__a=False							,						__a=None							,						__a=None							,						**__a							,						)			->			Optional[int]:






							'''simple docstring'''
							# TODO: remove this once the Hub files are updated.
							_UpperCamelCase									=    kwargs.pop('''text_config_dict'''							,						__a)
							_UpperCamelCase									=    kwargs.pop('''vision_config_dict'''							,						__a)

							super().__init__(**__a)
							_UpperCamelCase									=    share_cross_modal_transformer_layers
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    initializer_factor
							_UpperCamelCase									=    layer_norm_eps
							_UpperCamelCase									=    share_link_tower_layers
							_UpperCamelCase									=    link_tower_type
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    tie_word_embeddings
							_UpperCamelCase									=    init_layernorm_from_vision_encoder

							if text_config is None:
												_UpperCamelCase									=    {}
												logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.''')

							if vision_config is None:
												_UpperCamelCase									=    {}
												logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.''')

							_UpperCamelCase									=    BridgeTowerTextConfig(**__a)
							_UpperCamelCase									=    BridgeTowerVisionConfig(**__a)



		@classmethod
		def 		UpperCAmelCase				(   cls							,						__a							,						__a							,						**__a)			->			str:






							'''simple docstring'''
							return cls(text_config=text_config.to_dict()							,						vision_config=vision_config.to_dict()							,						**__a)




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    copy.deepcopy(self.__dict__)
							_UpperCamelCase									=    self.text_config.to_dict()
							_UpperCamelCase									=    self.vision_config.to_dict()
							_UpperCamelCase									=    self.__class__.model_type
							return output







"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						def is_in_circle(__lowerCAmelCase  ,       __lowerCAmelCase							) -> bool:
												lowerCamelCase__   =sqrt((x**2) + (y**2)							)
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						lowerCamelCase__   =mean(
						    int(is_in_circle(uniform(-1.0  ,       1.0							)  ,       uniform(-1.0  ,       1.0							)							)							)
						    for _ in range(__lowerCAmelCase							)							)
						# The ratio of the area for circle to square is pi/4.
						lowerCamelCase__   =proportion * 4
						print(F'''The estimated value of pi is {pi_estimate}'''							)
						print(F'''The numpy value of pi is {pi}'''							)
						print(F'''The total error is {abs(pi - pi_estimate							)}'''							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0  ,       )				->   float:




						'''simple docstring'''



						return mean(
						    function_to_integrate(uniform(__lowerCAmelCase  ,       __lowerCAmelCase							)							) for _ in range(__lowerCAmelCase							)							) * (max_value - min_value)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0							)				->   None:




						'''simple docstring'''



						def identity_function(__lowerCAmelCase							) -> float:
												return x

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =(max_value * max_value - min_value * min_value) / 2

						print("******************"							)
						print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {expected_value}'''							)
						print(F'''Total error is {abs(estimated_value - expected_value							)}'''							)
						print("******************"							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						def function_to_integrate(__lowerCAmelCase							) -> float:
												return sqrt(4.0 - x * x							)

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       0.0  ,       2.0							)

						print("******************"							)
						print("Estimating pi using area_under_curve_estimator"							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {pi}'''							)
						print(F'''Total error is {abs(estimated_value - pi							)}'''							)
						print("******************"							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


